Summary of Explainable Few-shot Learning Workflow For Detecting Invasive and Exotic Tree Species, by Caroline M. Gevaert et al.
Explainable few-shot learning workflow for detecting invasive and exotic tree species
by Caroline M. Gevaert, Alexandra Aguiar Pedro, Ou Ku, Hao Cheng, Pranav Chandramouli, Farzaneh Dadrass Javan, Francesco Nattino, Sonja Georgievska
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed workflow addresses the challenges of training deep learning models with minimal labeled data while providing explainable results for detecting invasive and exotic tree species in the Atlantic Forest of Brazil. The workflow combines a Siamese network with explainable AI (XAI) to enable classification with minimal labeled data and provide visual explanations for predictions. Results demonstrate the effectiveness of the proposed workflow, achieving a F1-score of 0.86 in 3-shot learning using a lightweight backbone like MobileNet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to use artificial intelligence (AI) and drones (UAVs) to help manage forests and protect endangered species. The method can work even when there’s very little data available, thanks to a special type of AI called explainable AI (XAI). This XAI helps us understand why the AI is making certain predictions. The results show that this approach works well for identifying new tree species in areas with limited data. |
Keywords
» Artificial intelligence » Classification » Deep learning » F1 score » Siamese network
